Joe | A Theme of Typecho - │ ├── config 主题外观、功能设置的样式脚本目录

MongoDB Compass has known issues on exporting an importing data for long time and it seems they are not willing to improve it!

When you try to export data using compass, it uses some sample documents to select the fields and if you are unlucky enough, you will miss some fields.

SOLUTION:

1. Use the Mongo DB Compass Aggregation tab to find all the existing fields in all documents:

   [{$project: { arrayofkeyvalue: { $objectToArray: '$$ROOT'} }},
   {$unwind: '$arrayofkeyvalue'},
   {$group: { _id: null, allkeys: { $addToSet: '$arrayofkeyvalue.k' } }}]

2. Add the fields from the 1st step to the Export Full Collection (Select Fields).

3. Export it!

Any time you write "for" and "pandas" anywhere close together you are probably doing something wrong.

It seems to me you want the cumulative count:

I believe one way to go about it is:

You should extend your declaration of generics to the "form" interfaces themselves.

In this case you need to give TypeScript a way to "infer" what the type of the data property of the form will be, in order for property to properly index it.

The way you have it written currently gives an error because you can't use keyof to extract the properties of a union type. Consider this example:

The problem here is that while the normal Fortran syntax allows the call PrintName(JoeJohnson) the actual type of the passed argument seems to be (setting a breakpoint within the printname and compiling gfortran)

The networkx solution you were referring to created an undirected graph, where the nodes were names and the edges were observed pairings. The clusters are then the connected components of the graph, which you can read off using a graph traversal algorithm (e.g. DFS, BFS).

So, you have two options:

1. Construct your own graph implementation, without using networkx, doing the above. Nothing I've just described are things that you can only do with networkx.

2. Use the disjoint sets data structure to generate the groupings. As the name suggests, we maintain a collection of disjoint sets (where a set represents a cluster of people). Whenever we see a pair, we union the clusters that the two people in the pairing originally belonged to. An implementation of union-find, as well as its application to the problem you've described, is given below:

From this toy Dataframe :

You can use

Find What: ^class boy.*(?:\R(?!}$).*)*\R\K(?=Height)
Replace With: #
. matches newline: DISABLED

Details:

• ^ - start of line
• class boy - class boy and then any zero or more chars as few as possible
• .* - the rest of line
• (?:\R(?!}$).*)* - zero or more lines up to end of file or to a line that is equal to }
• \R - a line break sequence
• \K - omit the text matched
• (?=Height) - immediately on the right, there must be Height.

This is your requirement